It is often desirable to make assessment and/or predictions regarding the operation of a real world physical system, such as an electro-mechanical system. For example, it may be helpful to predict a Remaining Useful Life (“RUL”) of an electro-mechanical system, such as an aircraft engine, to help plan when the system should be replaced. Likewise, an owner or operator of a system might want to monitor a condition of the system, or a portion of the system, to help make maintenance decisions, budget predictions, etc. Even with improvements in sensor and computer technologies, however, accurately making such assessments and/or predictions can be a difficult task. For example, an event that occurs while a system is not operating might impact the RUL and/or condition of the system but not be taken into account by typical approaches to system assessment and/or prediction processes.
Note that a real world physical system might be associated with system components, such as sensors and actuators. Increasingly, systems are becoming spatially distributed and these systems therefore include components that are significantly spatially distributed. As a consequence, there may be a need to provide an information transportation fabric that serves to sense, transport data, and control the spatially distributed components in order for the system to function efficiently and safely. The Internet of Things (“IoT”) may provide a new dimension of connectivity, but there may still be a need for a module that provides connectivity and computational intelligence for system components that are connected to the IoT.
It would therefore be desirable to provide systems and methods to facilitate assessments and/or predictions for a physical system associated with the IoT in an automatic and accurate manner.